Overview

Dataset statistics

Number of variables25
Number of observations1859
Missing cells5635
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory363.2 KiB
Average record size in memory200.1 B

Variable types

Categorical9
Numeric9
DateTime1
Unsupported6

Alerts

congress has constant value ""Constant
bill_number has a high cardinality: 718 distinct valuesHigh cardinality
vote_desc has a high cardinality: 868 distinct valuesHigh cardinality
vote_question has a high cardinality: 55 distinct valuesHigh cardinality
congress_url has a high cardinality: 879 distinct valuesHigh cardinality
rollnumber is highly overall correlated with clerk_rollnumber and 1 other fieldsHigh correlation
clerk_rollnumber is highly overall correlated with rollnumberHigh correlation
yea_count is highly overall correlated with chamber and 1 other fieldsHigh correlation
nay_count is highly overall correlated with nominate_log_likelihood and 2 other fieldsHigh correlation
nominate_log_likelihood is highly overall correlated with nay_countHigh correlation
chamber is highly overall correlated with yea_count and 3 other fieldsHigh correlation
session is highly overall correlated with rollnumberHigh correlation
vote_result is highly overall correlated with chamber and 1 other fieldsHigh correlation
vote_question is highly overall correlated with yea_count and 3 other fieldsHigh correlation
bill_number has 47 (2.5%) missing valuesMissing
vote_desc has 506 (27.2%) missing valuesMissing
dtl_desc has 1859 (100.0%) missing valuesMissing
issue_codes has 484 (26.0%) missing valuesMissing
crs_policy_area has 419 (22.5%) missing valuesMissing
crs_subjects has 413 (22.2%) missing valuesMissing
congress_url has 47 (2.5%) missing valuesMissing
source_documents has 1859 (100.0%) missing valuesMissing
vote_desc is uniformly distributedUniform
dtl_desc is an unsupported type, check if it needs cleaning or further analysisUnsupported
issue_codes is an unsupported type, check if it needs cleaning or further analysisUnsupported
peltzman_codes is an unsupported type, check if it needs cleaning or further analysisUnsupported
clausen_codes is an unsupported type, check if it needs cleaning or further analysisUnsupported
crs_subjects is an unsupported type, check if it needs cleaning or further analysisUnsupported
source_documents is an unsupported type, check if it needs cleaning or further analysisUnsupported
nay_count has 182 (9.8%) zerosZeros
nominate_mid_1 has 286 (15.4%) zerosZeros
nominate_mid_2 has 287 (15.4%) zerosZeros
nominate_spread_1 has 286 (15.4%) zerosZeros
nominate_spread_2 has 286 (15.4%) zerosZeros
nominate_log_likelihood has 286 (15.4%) zerosZeros

Reproduction

Analysis started2023-08-28 21:43:46.939550
Analysis finished2023-08-28 21:43:56.799955
Duration9.86 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

congress
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
113
1859 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5577
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row113
2nd row113
3rd row113
4th row113
5th row113

Common Values

ValueCountFrequency (%)
113 1859
100.0%

Length

2023-08-28T14:43:56.846638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:43:56.933631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
113 1859
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3718
66.7%
3 1859
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5577
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3718
66.7%
3 1859
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3718
66.7%
3 1859
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3718
66.7%
3 1859
33.3%

chamber
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
House
1202 
Senate
657 

Length

Max length6
Median length5
Mean length5.3534158
Min length5

Characters and Unicode

Total characters9952
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowHouse
4th rowHouse
5th rowHouse

Common Values

ValueCountFrequency (%)
House 1202
64.7%
Senate 657
35.3%

Length

2023-08-28T14:43:57.006312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:43:57.102352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
house 1202
64.7%
senate 657
35.3%

Most occurring characters

ValueCountFrequency (%)
e 2516
25.3%
H 1202
12.1%
o 1202
12.1%
u 1202
12.1%
s 1202
12.1%
S 657
 
6.6%
n 657
 
6.6%
a 657
 
6.6%
t 657
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8093
81.3%
Uppercase Letter 1859
 
18.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2516
31.1%
o 1202
14.9%
u 1202
14.9%
s 1202
14.9%
n 657
 
8.1%
a 657
 
8.1%
t 657
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
H 1202
64.7%
S 657
35.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 9952
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2516
25.3%
H 1202
12.1%
o 1202
12.1%
u 1202
12.1%
s 1202
12.1%
S 657
 
6.6%
n 657
 
6.6%
a 657
 
6.6%
t 657
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2516
25.3%
H 1202
12.1%
o 1202
12.1%
u 1202
12.1%
s 1202
12.1%
S 657
 
6.6%
n 657
 
6.6%
a 657
 
6.6%
t 657
 
6.6%

rollnumber
Real number (ℝ)

Distinct1202
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.19419
Minimum1
Maximum1202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2023-08-28T14:43:57.190177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47
Q1233
median465
Q3737.5
95-th percentile1109.1
Maximum1202
Range1201
Interquartile range (IQR)504.5

Descriptive statistics

Standard deviation328.00594
Coefficient of variation (CV)0.64926705
Kurtosis-0.85503307
Mean505.19419
Median Absolute Deviation (MAD)246
Skewness0.40918579
Sum939156
Variance107587.9
MonotonicityNot monotonic
2023-08-28T14:43:57.306734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.1%
433 2
 
0.1%
435 2
 
0.1%
436 2
 
0.1%
437 2
 
0.1%
438 2
 
0.1%
439 2
 
0.1%
440 2
 
0.1%
441 2
 
0.1%
442 2
 
0.1%
Other values (1192) 1839
98.9%
ValueCountFrequency (%)
1 2
0.1%
2 2
0.1%
3 2
0.1%
4 2
0.1%
5 2
0.1%
6 2
0.1%
7 2
0.1%
8 2
0.1%
9 2
0.1%
10 2
0.1%
ValueCountFrequency (%)
1202 1
0.1%
1201 1
0.1%
1200 1
0.1%
1199 1
0.1%
1198 1
0.1%
1197 1
0.1%
1196 1
0.1%
1195 1
0.1%
1194 1
0.1%
1193 1
0.1%

date
Date

Distinct285
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
Minimum2013-01-03 00:00:00
Maximum2014-12-16 00:00:00
2023-08-28T14:43:57.427387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:57.535359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

session
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
1
931 
2
928 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1859
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 931
50.1%
2 928
49.9%

Length

2023-08-28T14:43:57.641229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T14:43:57.731654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 931
50.1%
2 928
49.9%

Most occurring characters

ValueCountFrequency (%)
1 931
50.1%
2 928
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1859
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 931
50.1%
2 928
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1859
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 931
50.1%
2 928
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 931
50.1%
2 928
49.9%

clerk_rollnumber
Real number (ℝ)

Distinct641
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.22378
Minimum1
Maximum641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2023-08-28T14:43:57.824245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1117
median233
Q3370.5
95-th percentile556.1
Maximum641
Range640
Interquartile range (IQR)253.5

Descriptive statistics

Standard deviation166.41453
Coefficient of variation (CV)0.65203381
Kurtosis-0.79500774
Mean255.22378
Median Absolute Deviation (MAD)125
Skewness0.434297
Sum474461
Variance27693.796
MonotonicityNot monotonic
2023-08-28T14:43:57.941640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
0.2%
220 4
 
0.2%
200 4
 
0.2%
199 4
 
0.2%
198 4
 
0.2%
197 4
 
0.2%
196 4
 
0.2%
195 4
 
0.2%
194 4
 
0.2%
193 4
 
0.2%
Other values (631) 1819
97.8%
ValueCountFrequency (%)
1 2
0.1%
2 4
0.2%
3 4
0.2%
4 4
0.2%
5 4
0.2%
6 4
0.2%
7 4
0.2%
8 4
0.2%
9 4
0.2%
10 4
0.2%
ValueCountFrequency (%)
641 1
0.1%
640 1
0.1%
639 1
0.1%
638 1
0.1%
637 1
0.1%
636 1
0.1%
635 1
0.1%
634 1
0.1%
633 1
0.1%
632 1
0.1%

yea_count
Real number (ℝ)

Distinct340
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.19365
Minimum0
Maximum426
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2023-08-28T14:43:58.063430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q171
median188
Q3233
95-th percentile406
Maximum426
Range426
Interquartile range (IQR)162

Descriptive statistics

Standard deviation112.21165
Coefficient of variation (CV)0.6158922
Kurtosis-0.59350419
Mean182.19365
Median Absolute Deviation (MAD)92
Skewness0.54164022
Sum338698
Variance12591.454
MonotonicityNot monotonic
2023-08-28T14:43:58.168409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 48
 
2.6%
56 33
 
1.8%
227 32
 
1.7%
52 31
 
1.7%
53 30
 
1.6%
226 30
 
1.6%
55 29
 
1.6%
229 27
 
1.5%
194 25
 
1.3%
228 24
 
1.3%
Other values (330) 1550
83.4%
ValueCountFrequency (%)
0 2
0.1%
1 2
0.1%
2 1
0.1%
4 1
0.1%
7 1
0.1%
10 1
0.1%
18 2
0.1%
22 1
0.1%
23 1
0.1%
24 1
0.1%
ValueCountFrequency (%)
426 1
 
0.1%
425 2
 
0.1%
424 1
 
0.1%
423 5
0.3%
422 5
0.3%
421 2
 
0.1%
420 8
0.4%
419 2
 
0.1%
418 6
0.3%
417 3
 
0.2%

nay_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct306
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.02905
Minimum0
Maximum421
Zeros182
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2023-08-28T14:43:58.279212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median114
Q3201.5
95-th percentile266
Maximum421
Range421
Interquartile range (IQR)170.5

Descriptive statistics

Standard deviation99.393756
Coefficient of variation (CV)0.82808085
Kurtosis-1.1854255
Mean120.02905
Median Absolute Deviation (MAD)85
Skewness0.29215405
Sum223134
Variance9879.1186
MonotonicityNot monotonic
2023-08-28T14:43:58.386721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 182
 
9.8%
42 40
 
2.2%
41 37
 
2.0%
192 35
 
1.9%
43 32
 
1.7%
1 31
 
1.7%
40 29
 
1.6%
194 27
 
1.5%
191 25
 
1.3%
3 25
 
1.3%
Other values (296) 1396
75.1%
ValueCountFrequency (%)
0 182
9.8%
1 31
 
1.7%
2 23
 
1.2%
3 25
 
1.3%
4 13
 
0.7%
5 10
 
0.5%
6 6
 
0.3%
7 10
 
0.5%
8 7
 
0.4%
9 3
 
0.2%
ValueCountFrequency (%)
421 1
0.1%
419 1
0.1%
416 1
0.1%
415 1
0.1%
413 1
0.1%
409 1
0.1%
397 1
0.1%
381 1
0.1%
373 1
0.1%
372 2
0.1%

nominate_mid_1
Real number (ℝ)

Distinct820
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034949435
Minimum-0.991
Maximum0.998
Zeros286
Zeros (%)15.4%
Negative720
Negative (%)38.7%
Memory size14.6 KiB
2023-08-28T14:43:58.501417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.991
5-th percentile-0.445
Q1-0.102
median0
Q30.145
95-th percentile0.7221
Maximum0.998
Range1.989
Interquartile range (IQR)0.247

Descriptive statistics

Standard deviation0.31304493
Coefficient of variation (CV)8.9570812
Kurtosis1.8761235
Mean0.034949435
Median Absolute Deviation (MAD)0.126
Skewness0.45891132
Sum64.971
Variance0.097997128
MonotonicityNot monotonic
2023-08-28T14:43:58.620566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
15.4%
0.059 15
 
0.8%
0.014 14
 
0.8%
0.056 12
 
0.6%
0.06 9
 
0.5%
0.01 9
 
0.5%
0.057 8
 
0.4%
-0.038 8
 
0.4%
-0.036 7
 
0.4%
0.064 7
 
0.4%
Other values (810) 1484
79.8%
ValueCountFrequency (%)
-0.991 1
0.1%
-0.981 1
0.1%
-0.98 1
0.1%
-0.971 1
0.1%
-0.97 1
0.1%
-0.954 1
0.1%
-0.919 1
0.1%
-0.911 1
0.1%
-0.904 2
0.1%
-0.902 1
0.1%
ValueCountFrequency (%)
0.998 1
0.1%
0.994 1
0.1%
0.992 1
0.1%
0.988 2
0.1%
0.984 1
0.1%
0.983 1
0.1%
0.971 1
0.1%
0.968 1
0.1%
0.965 1
0.1%
0.962 1
0.1%

nominate_mid_2
Real number (ℝ)

Distinct1007
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.116305
Minimum-1
Maximum1
Zeros287
Zeros (%)15.4%
Negative645
Negative (%)34.7%
Memory size14.6 KiB
2023-08-28T14:43:58.740667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.8704
Q1-0.1645
median0
Q30.468
95-th percentile0.987
Maximum1
Range2
Interquartile range (IQR)0.6325

Descriptive statistics

Standard deviation0.51104198
Coefficient of variation (CV)4.3939811
Kurtosis-0.39391854
Mean0.116305
Median Absolute Deviation (MAD)0.306
Skewness-0.06024911
Sum216.211
Variance0.26116391
MonotonicityNot monotonic
2023-08-28T14:43:59.040226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 287
 
15.4%
0.999 17
 
0.9%
1 16
 
0.9%
-0.223 11
 
0.6%
0.009 9
 
0.5%
0.998 9
 
0.5%
0.997 8
 
0.4%
0.994 8
 
0.4%
0.991 7
 
0.4%
-0.242 7
 
0.4%
Other values (997) 1480
79.6%
ValueCountFrequency (%)
-1 5
0.3%
-0.999 5
0.3%
-0.998 2
 
0.1%
-0.997 1
 
0.1%
-0.996 1
 
0.1%
-0.995 2
 
0.1%
-0.994 3
0.2%
-0.993 1
 
0.1%
-0.992 1
 
0.1%
-0.991 1
 
0.1%
ValueCountFrequency (%)
1 16
0.9%
0.999 17
0.9%
0.998 9
0.5%
0.997 8
0.4%
0.996 3
 
0.2%
0.995 6
 
0.3%
0.994 8
0.4%
0.993 4
 
0.2%
0.992 2
 
0.1%
0.991 7
0.4%

nominate_spread_1
Real number (ℝ)

Distinct1043
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098418505
Minimum-2.253
Maximum1.993
Zeros286
Zeros (%)15.4%
Negative655
Negative (%)35.2%
Memory size14.6 KiB
2023-08-28T14:43:59.162361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.253
5-th percentile-1.0474
Q1-0.2895
median0
Q30.6365
95-th percentile1.2631
Maximum1.993
Range4.246
Interquartile range (IQR)0.926

Descriptive statistics

Standard deviation0.66219762
Coefficient of variation (CV)6.7283853
Kurtosis-0.15727931
Mean0.098418505
Median Absolute Deviation (MAD)0.422
Skewness0.074519219
Sum182.96
Variance0.43850569
MonotonicityNot monotonic
2023-08-28T14:43:59.267773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
15.4%
0.666 12
 
0.6%
-0.728 10
 
0.5%
-0.729 10
 
0.5%
-0.726 9
 
0.5%
0.735 8
 
0.4%
0.672 8
 
0.4%
0.728 8
 
0.4%
0.727 8
 
0.4%
-0.65 8
 
0.4%
Other values (1033) 1492
80.3%
ValueCountFrequency (%)
-2.253 1
0.1%
-1.666 1
0.1%
-1.658 1
0.1%
-1.591 1
0.1%
-1.51 1
0.1%
-1.497 1
0.1%
-1.465 1
0.1%
-1.409 1
0.1%
-1.399 2
0.1%
-1.388 1
0.1%
ValueCountFrequency (%)
1.993 1
0.1%
1.909 1
0.1%
1.866 1
0.1%
1.851 1
0.1%
1.829 1
0.1%
1.813 1
0.1%
1.809 1
0.1%
1.761 1
0.1%
1.749 1
0.1%
1.744 1
0.1%

nominate_spread_2
Real number (ℝ)

Distinct1151
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0729844
Minimum-6.046
Maximum3.674
Zeros286
Zeros (%)15.4%
Negative700
Negative (%)37.7%
Memory size14.6 KiB
2023-08-28T14:43:59.379119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6.046
5-th percentile-1.0901
Q1-0.2725
median0
Q30.375
95-th percentile1.4301
Maximum3.674
Range9.72
Interquartile range (IQR)0.6475

Descriptive statistics

Standard deviation0.73311485
Coefficient of variation (CV)10.044816
Kurtosis4.5608004
Mean0.0729844
Median Absolute Deviation (MAD)0.332
Skewness-0.022164763
Sum135.678
Variance0.53745739
MonotonicityNot monotonic
2023-08-28T14:43:59.507442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
15.4%
-0.713 9
 
0.5%
-0.72 8
 
0.4%
0.652 7
 
0.4%
-0.099 6
 
0.3%
0.328 5
 
0.3%
-0.145 5
 
0.3%
0.329 4
 
0.2%
-0.152 4
 
0.2%
0.108 4
 
0.2%
Other values (1141) 1521
81.8%
ValueCountFrequency (%)
-6.046 1
0.1%
-3.313 1
0.1%
-2.974 1
0.1%
-2.819 2
0.1%
-2.335 1
0.1%
-2.311 1
0.1%
-2.292 1
0.1%
-2.27 1
0.1%
-2.08 1
0.1%
-1.987 1
0.1%
ValueCountFrequency (%)
3.674 1
0.1%
3.673 1
0.1%
3.486 1
0.1%
2.745 1
0.1%
2.704 1
0.1%
2.491 1
0.1%
2.473 1
0.1%
2.44 1
0.1%
2.348 1
0.1%
2.267 1
0.1%

nominate_log_likelihood
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1461
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.252442
Minimum-305.162
Maximum0
Zeros286
Zeros (%)15.4%
Negative1573
Negative (%)84.6%
Memory size14.6 KiB
2023-08-28T14:43:59.632535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-305.162
5-th percentile-182.465
Q1-47.9825
median-15.747
Q3-1.058
95-th percentile0
Maximum0
Range305.162
Interquartile range (IQR)46.9245

Descriptive statistics

Standard deviation60.223055
Coefficient of variation (CV)-1.4961342
Kurtosis4.1586421
Mean-40.252442
Median Absolute Deviation (MAD)15.625
Skewness-2.13572
Sum-74829.29
Variance3626.8164
MonotonicityNot monotonic
2023-08-28T14:43:59.740176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
15.4%
-0.122 11
 
0.6%
-0.127 10
 
0.5%
-0.129 9
 
0.5%
-0.121 7
 
0.4%
-0.317 5
 
0.3%
-0.123 4
 
0.2%
-0.289 4
 
0.2%
-0.303 4
 
0.2%
-0.293 3
 
0.2%
Other values (1451) 1516
81.5%
ValueCountFrequency (%)
-305.162 1
0.1%
-293.368 1
0.1%
-285.408 1
0.1%
-281.611 1
0.1%
-276.325 1
0.1%
-272.076 1
0.1%
-268.009 1
0.1%
-267.827 1
0.1%
-266.375 1
0.1%
-266.125 1
0.1%
ValueCountFrequency (%)
0 286
15.4%
-0.101 1
 
0.1%
-0.102 1
 
0.1%
-0.108 2
 
0.1%
-0.109 1
 
0.1%
-0.112 1
 
0.1%
-0.115 1
 
0.1%
-0.116 2
 
0.1%
-0.117 2
 
0.1%
-0.118 2
 
0.1%

bill_number
Categorical

HIGH CARDINALITY  MISSING 

Distinct718
Distinct (%)39.6%
Missing47
Missing (%)2.5%
Memory size14.6 KiB
SCONRES8
 
48
HR2609
 
35
HR2397
 
34
HR1947
 
31
HR4923
 
29
Other values (713)
1635 

Length

Max length10
Median length6
Mean length5.8449227
Min length3

Characters and Unicode

Total characters10591
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique337 ?
Unique (%)18.6%

Sample

1st rowHRES5
2nd rowHRES5
3rd rowHRES5
4th rowHRES5
5th rowHR41

Common Values

ValueCountFrequency (%)
SCONRES8 48
 
2.6%
HR2609 35
 
1.9%
HR2397 34
 
1.8%
HR1947 31
 
1.7%
HR4923 29
 
1.6%
HR4660 28
 
1.5%
HR1960 23
 
1.2%
HR4745 23
 
1.2%
HR4870 21
 
1.1%
S744 18
 
1.0%
Other values (708) 1522
81.9%
(Missing) 47
 
2.5%

Length

2023-08-28T14:43:59.849647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sconres8 48
 
2.6%
hr2609 35
 
1.9%
hr2397 34
 
1.9%
hr1947 31
 
1.7%
hr4923 29
 
1.6%
hr4660 28
 
1.5%
hr1960 23
 
1.3%
hr4745 23
 
1.3%
hr4870 21
 
1.2%
s744 18
 
1.0%
Other values (708) 1522
84.0%

Most occurring characters

ValueCountFrequency (%)
R 1288
12.2%
H 1231
11.6%
1 952
 
9.0%
2 811
 
7.7%
4 724
 
6.8%
3 621
 
5.9%
9 574
 
5.4%
S 552
 
5.2%
0 528
 
5.0%
7 524
 
4.9%
Other values (9) 2786
26.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6191
58.5%
Uppercase Letter 4400
41.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 952
15.4%
2 811
13.1%
4 724
11.7%
3 621
10.0%
9 574
9.3%
0 528
8.5%
7 524
8.5%
6 521
8.4%
5 489
7.9%
8 447
7.2%
Uppercase Letter
ValueCountFrequency (%)
R 1288
29.3%
H 1231
28.0%
S 552
12.5%
N 434
 
9.9%
P 366
 
8.3%
E 337
 
7.7%
C 68
 
1.5%
O 68
 
1.5%
J 56
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 6191
58.5%
Latin 4400
41.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 952
15.4%
2 811
13.1%
4 724
11.7%
3 621
10.0%
9 574
9.3%
0 528
8.5%
7 524
8.5%
6 521
8.4%
5 489
7.9%
8 447
7.2%
Latin
ValueCountFrequency (%)
R 1288
29.3%
H 1231
28.0%
S 552
12.5%
N 434
 
9.9%
P 366
 
8.3%
E 337
 
7.7%
C 68
 
1.5%
O 68
 
1.5%
J 56
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1288
12.2%
H 1231
11.6%
1 952
 
9.0%
2 811
 
7.7%
4 724
 
6.8%
3 621
 
5.9%
9 574
 
5.4%
S 552
 
5.2%
0 528
 
5.0%
7 524
 
4.9%
Other values (9) 2786
26.3%

vote_result
Categorical

Distinct32
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
Passed
612 
Failed
449 
Nomination Confirmed
188 
Cloture Motion Agreed to
171 
Agreed to
140 
Other values (27)
299 

Length

Max length44
Median length6
Mean length11.871436
Min length6

Characters and Unicode

Total characters22069
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.5%

Sample

1st rowBoehner
2nd rowPassed
3rd rowPassed
4th rowFailed
5th rowPassed

Common Values

ValueCountFrequency (%)
Passed 612
32.9%
Failed 449
24.2%
Nomination Confirmed 188
 
10.1%
Cloture Motion Agreed to 171
 
9.2%
Agreed to 140
 
7.5%
Amendment Rejected 73
 
3.9%
Amendment Agreed to 43
 
2.3%
Motion to Proceed Agreed to 34
 
1.8%
Cloture Motion Rejected 24
 
1.3%
Bill Passed 23
 
1.2%
Other values (22) 102
 
5.5%

Length

2023-08-28T14:43:59.963426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
passed 638
18.5%
to 543
15.8%
failed 460
13.4%
agreed 451
13.1%
motion 314
9.1%
cloture 218
 
6.3%
confirmed 188
 
5.5%
nomination 188
 
5.5%
amendment 116
 
3.4%
rejected 115
 
3.3%
Other values (27) 213
 
6.2%

Most occurring characters

ValueCountFrequency (%)
e 3205
14.5%
o 2084
 
9.4%
d 2041
 
9.2%
1585
 
7.2%
t 1550
 
7.0%
i 1396
 
6.3%
a 1326
 
6.0%
s 1296
 
5.9%
n 1176
 
5.3%
r 940
 
4.3%
Other values (25) 5470
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17638
79.9%
Uppercase Letter 2846
 
12.9%
Space Separator 1585
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3205
18.2%
o 2084
11.8%
d 2041
11.6%
t 1550
8.8%
i 1396
7.9%
a 1326
7.5%
s 1296
7.3%
n 1176
 
6.7%
r 940
 
5.3%
l 762
 
4.3%
Other values (10) 1862
10.6%
Uppercase Letter
ValueCountFrequency (%)
P 697
24.5%
A 572
20.1%
F 460
16.2%
C 414
14.5%
M 314
11.0%
N 190
 
6.7%
R 133
 
4.7%
T 27
 
0.9%
B 25
 
0.9%
D 5
 
0.2%
Other values (4) 9
 
0.3%
Space Separator
ValueCountFrequency (%)
1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20484
92.8%
Common 1585
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3205
15.6%
o 2084
10.2%
d 2041
10.0%
t 1550
 
7.6%
i 1396
 
6.8%
a 1326
 
6.5%
s 1296
 
6.3%
n 1176
 
5.7%
r 940
 
4.6%
l 762
 
3.7%
Other values (24) 4708
23.0%
Common
ValueCountFrequency (%)
1585
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3205
14.5%
o 2084
 
9.4%
d 2041
 
9.2%
1585
 
7.2%
t 1550
 
7.0%
i 1396
 
6.3%
a 1326
 
6.0%
s 1296
 
5.9%
n 1176
 
5.3%
r 940
 
4.3%
Other values (25) 5470
24.8%

vote_desc
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct868
Distinct (%)64.2%
Missing506
Missing (%)27.2%
Memory size14.6 KiB
To require the Secretary of the Interior to assemble a team of technical, policy, and financial experts to address the energy needs of the insular areas of the United States and the Freely Associated States through the development of energy action plans aimed at promoting access to affordable, reliable energy, including increasing use of indigenous clean-energy resources, and for other purposes.
 
15
A joint resolution making continuing appropriations for fiscal year 2014, and for other purposes.
 
10
Of a perfecting nature.
 
10
Patricia Ann Millett, of Virginia, to be United States Circuit Judge for the District of Columbia Circuit
 
8
A bill to amend the Internal Revenue Code of 1986 to ensure that emergency services volunteers are not taken into account as employees under the shared responsibility requirements contained in the Patient Protection and Affordable Care Act.
 
8
Other values (863)
1302 

Length

Max length543
Median length242
Mean length132.73245
Min length10

Characters and Unicode

Total characters179587
Distinct characters76
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique489 ?
Unique (%)36.1%

Sample

1st rowAdopting rules for the One Hundred Thirteenth Congress
2nd rowAdopting rules for the One Hundred Thirteenth Congress
3rd rowAdopting rules for the One Hundred Thirteenth Congress
4th rowAdopting rules for the One Hundred Thirteenth Congress
5th rowTo temporarily increase the borrowing authority of the Federal Emergency Management Agency for carrying out the National Flood Insurance Program

Common Values

ValueCountFrequency (%)
To require the Secretary of the Interior to assemble a team of technical, policy, and financial experts to address the energy needs of the insular areas of the United States and the Freely Associated States through the development of energy action plans aimed at promoting access to affordable, reliable energy, including increasing use of indigenous clean-energy resources, and for other purposes. 15
 
0.8%
A joint resolution making continuing appropriations for fiscal year 2014, and for other purposes. 10
 
0.5%
Of a perfecting nature. 10
 
0.5%
Patricia Ann Millett, of Virginia, to be United States Circuit Judge for the District of Columbia Circuit 8
 
0.4%
A bill to amend the Internal Revenue Code of 1986 to ensure that emergency services volunteers are not taken into account as employees under the shared responsibility requirements contained in the Patient Protection and Affordable Care Act. 8
 
0.4%
A bill to provide for comprehensive immigration reform and for other purposes. 7
 
0.4%
In the nature of a substitute. 7
 
0.4%
Federal Agriculture Reform and Risk Management Act 7
 
0.4%
Veterans’ Access to Care through Choice, Accountability, and Transparency Act of 2014 6
 
0.3%
Melvin L. Watt, of North Carolina, to be Director of the Federal Housing Finance Agency for a term of five years 6
 
0.3%
Other values (858) 1269
68.3%
(Missing) 506
 
27.2%

Length

2023-08-28T14:44:00.115648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 2170
 
7.7%
the 1970
 
7.0%
to 1473
 
5.3%
for 1438
 
5.1%
and 1121
 
4.0%
act 502
 
1.8%
a 409
 
1.5%
other 388
 
1.4%
be 381
 
1.4%
district 376
 
1.3%
Other values (2893) 17816
63.5%

Most occurring characters

ValueCountFrequency (%)
26692
14.9%
e 16353
 
9.1%
t 13487
 
7.5%
o 12921
 
7.2%
r 11512
 
6.4%
i 11147
 
6.2%
n 10597
 
5.9%
a 10151
 
5.7%
s 7221
 
4.0%
d 4985
 
2.8%
Other values (66) 54521
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135215
75.3%
Space Separator 26692
 
14.9%
Uppercase Letter 10206
 
5.7%
Decimal Number 3671
 
2.0%
Other Punctuation 3247
 
1.8%
Open Punctuation 160
 
0.1%
Close Punctuation 160
 
0.1%
Dash Punctuation 148
 
0.1%
Final Punctuation 61
 
< 0.1%
Initial Punctuation 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16353
12.1%
t 13487
10.0%
o 12921
9.6%
r 11512
 
8.5%
i 11147
 
8.2%
n 10597
 
7.8%
a 10151
 
7.5%
s 7221
 
5.3%
d 4985
 
3.7%
f 4964
 
3.7%
Other values (16) 31877
23.6%
Uppercase Letter
ValueCountFrequency (%)
A 1260
12.3%
S 1069
 
10.5%
C 844
 
8.3%
R 772
 
7.6%
D 720
 
7.1%
P 590
 
5.8%
H 564
 
5.5%
T 538
 
5.3%
M 502
 
4.9%
J 431
 
4.2%
Other values (15) 2916
28.6%
Decimal Number
ValueCountFrequency (%)
1 746
20.3%
0 687
18.7%
2 660
18.0%
4 349
9.5%
3 344
9.4%
9 233
 
6.3%
5 187
 
5.1%
6 171
 
4.7%
8 159
 
4.3%
7 135
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 1972
60.7%
. 1161
35.8%
; 89
 
2.7%
' 14
 
0.4%
" 6
 
0.2%
: 3
 
0.1%
& 2
 
0.1%
Final Punctuation
ValueCountFrequency (%)
42
68.9%
19
31.1%
Space Separator
ValueCountFrequency (%)
26692
100.0%
Open Punctuation
ValueCountFrequency (%)
( 160
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Initial Punctuation
ValueCountFrequency (%)
19
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 145421
81.0%
Common 34166
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16353
 
11.2%
t 13487
 
9.3%
o 12921
 
8.9%
r 11512
 
7.9%
i 11147
 
7.7%
n 10597
 
7.3%
a 10151
 
7.0%
s 7221
 
5.0%
d 4985
 
3.4%
f 4964
 
3.4%
Other values (41) 42083
28.9%
Common
ValueCountFrequency (%)
26692
78.1%
, 1972
 
5.8%
. 1161
 
3.4%
1 746
 
2.2%
0 687
 
2.0%
2 660
 
1.9%
4 349
 
1.0%
3 344
 
1.0%
9 233
 
0.7%
5 187
 
0.5%
Other values (15) 1135
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179507
> 99.9%
Punctuation 80
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26692
14.9%
e 16353
 
9.1%
t 13487
 
7.5%
o 12921
 
7.2%
r 11512
 
6.4%
i 11147
 
6.2%
n 10597
 
5.9%
a 10151
 
5.7%
s 7221
 
4.0%
d 4985
 
2.8%
Other values (63) 54441
30.3%
Punctuation
ValueCountFrequency (%)
42
52.5%
19
23.8%
19
23.8%

vote_question
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct55
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
On Agreeing to the Amendment
458 
On the Cloture Motion
195 
On the Nomination
188 
On Passage
139 
On the Amendment
116 
Other values (50)
763 

Length

Max length89
Median length66
Mean length26.891339
Min length10

Characters and Unicode

Total characters49991
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)1.1%

Sample

1st rowElection of the Speaker
2nd rowOn Motion to Table the Motion to Refer
3rd rowOn Ordering the Previous Question
4th rowOn Motion to Commit
5th rowOn Agreeing to the Resolution

Common Values

ValueCountFrequency (%)
On Agreeing to the Amendment 458
24.6%
On the Cloture Motion 195
10.5%
On the Nomination 188
10.1%
On Passage 139
 
7.5%
On the Amendment 116
 
6.2%
On Agreeing to the Resolution 107
 
5.8%
On Motion to Suspend the Rules and Pass 107
 
5.8%
On Motion to Recommit with Instructions 103
 
5.5%
On Ordering the Previous Question 86
 
4.6%
On Motion to Suspend the Rules and Pass, as Amended 85
 
4.6%
Other values (45) 275
14.8%

Length

2023-08-28T14:44:00.254400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
on 1869
20.4%
the 1638
17.9%
to 1024
11.2%
motion 669
 
7.3%
amendment 595
 
6.5%
agreeing 572
 
6.3%
and 221
 
2.4%
cloture 218
 
2.4%
suspend 218
 
2.4%
rules 218
 
2.4%
Other values (62) 1908
20.9%

Most occurring characters

ValueCountFrequency (%)
7291
14.6%
n 5938
11.9%
e 5816
11.6%
t 5032
10.1%
o 3866
 
7.7%
i 2455
 
4.9%
O 1933
 
3.9%
s 1772
 
3.5%
h 1769
 
3.5%
m 1704
 
3.4%
Other values (39) 12415
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36660
73.3%
Space Separator 7291
 
14.6%
Uppercase Letter 5939
 
11.9%
Other Punctuation 97
 
0.2%
Decimal Number 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5938
16.2%
e 5816
15.9%
t 5032
13.7%
o 3866
10.5%
i 2455
6.7%
s 1772
 
4.8%
h 1769
 
4.8%
m 1704
 
4.6%
g 1466
 
4.0%
r 1398
 
3.8%
Other values (13) 5444
14.8%
Uppercase Letter
ValueCountFrequency (%)
O 1933
32.5%
A 1347
22.7%
M 669
 
11.3%
P 500
 
8.4%
R 476
 
8.0%
C 273
 
4.6%
S 240
 
4.0%
N 188
 
3.2%
I 110
 
1.9%
Q 86
 
1.4%
Other values (9) 117
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 95
97.9%
# 2
 
2.1%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
7291
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42599
85.2%
Common 7392
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5938
13.9%
e 5816
13.7%
t 5032
11.8%
o 3866
 
9.1%
i 2455
 
5.8%
O 1933
 
4.5%
s 1772
 
4.2%
h 1769
 
4.2%
m 1704
 
4.0%
g 1466
 
3.4%
Other values (32) 10848
25.5%
Common
ValueCountFrequency (%)
7291
98.6%
, 95
 
1.3%
# 2
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%
2 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7291
14.6%
n 5938
11.9%
e 5816
11.6%
t 5032
10.1%
o 3866
 
7.7%
i 2455
 
4.9%
O 1933
 
3.9%
s 1772
 
3.5%
h 1769
 
3.5%
m 1704
 
3.4%
Other values (39) 12415
24.8%

dtl_desc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1859
Missing (%)100.0%
Memory size14.6 KiB

issue_codes
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing484
Missing (%)26.0%
Memory size14.6 KiB

peltzman_codes
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.1%
Memory size14.6 KiB

clausen_codes
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size14.6 KiB

crs_policy_area
Categorical

Distinct31
Distinct (%)2.2%
Missing419
Missing (%)22.5%
Memory size14.6 KiB
Economics and Public Finance
322 
Congress
211 
Armed Forces and National Security
178 
Energy
79 
Government Operations and Politics
69 
Other values (26)
581 

Length

Max length43
Median length35
Mean length21.995139
Min length3

Characters and Unicode

Total characters31673
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st rowCongress
2nd rowCongress
3rd rowCongress
4th rowCongress
5th rowEmergency Management

Common Values

ValueCountFrequency (%)
Economics and Public Finance 322
17.3%
Congress 211
11.4%
Armed Forces and National Security 178
9.6%
Energy 79
 
4.2%
Government Operations and Politics 69
 
3.7%
Finance and Financial Sector 57
 
3.1%
Public Lands and Natural Resources 57
 
3.1%
Agriculture and Food 56
 
3.0%
Emergency Management 45
 
2.4%
Environmental Protection 41
 
2.2%
Other values (21) 325
17.5%
(Missing) 419
22.5%

Length

2023-08-28T14:44:00.374712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 831
19.3%
public 407
 
9.5%
finance 380
 
8.8%
economics 322
 
7.5%
congress 211
 
4.9%
armed 178
 
4.1%
forces 178
 
4.1%
national 178
 
4.1%
security 178
 
4.1%
resources 90
 
2.1%
Other values (53) 1345
31.3%

Most occurring characters

ValueCountFrequency (%)
n 3467
 
10.9%
2858
 
9.0%
a 2500
 
7.9%
e 2370
 
7.5%
c 2321
 
7.3%
i 2239
 
7.1%
o 2180
 
6.9%
r 1716
 
5.4%
s 1421
 
4.5%
t 1295
 
4.1%
Other values (32) 9306
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25326
80.0%
Uppercase Letter 3467
 
10.9%
Space Separator 2858
 
9.0%
Other Punctuation 22
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3467
13.7%
a 2500
9.9%
e 2370
9.4%
c 2321
9.2%
i 2239
8.8%
o 2180
8.6%
r 1716
 
6.8%
s 1421
 
5.6%
t 1295
 
5.1%
d 1156
 
4.6%
Other values (13) 4661
18.4%
Uppercase Letter
ValueCountFrequency (%)
F 682
19.7%
E 579
16.7%
P 517
14.9%
A 271
 
7.8%
C 262
 
7.6%
S 248
 
7.2%
N 243
 
7.0%
L 124
 
3.6%
R 94
 
2.7%
T 77
 
2.2%
Other values (7) 370
10.7%
Space Separator
ValueCountFrequency (%)
2858
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28793
90.9%
Common 2880
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3467
12.0%
a 2500
 
8.7%
e 2370
 
8.2%
c 2321
 
8.1%
i 2239
 
7.8%
o 2180
 
7.6%
r 1716
 
6.0%
s 1421
 
4.9%
t 1295
 
4.5%
d 1156
 
4.0%
Other values (30) 8128
28.2%
Common
ValueCountFrequency (%)
2858
99.2%
, 22
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31673
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3467
 
10.9%
2858
 
9.0%
a 2500
 
7.9%
e 2370
 
7.5%
c 2321
 
7.3%
i 2239
 
7.1%
o 2180
 
6.9%
r 1716
 
5.4%
s 1421
 
4.5%
t 1295
 
4.1%
Other values (32) 9306
29.4%

crs_subjects
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing413
Missing (%)22.2%
Memory size14.6 KiB

congress_url
Categorical

HIGH CARDINALITY  MISSING 

Distinct879
Distinct (%)48.5%
Missing47
Missing (%)2.5%
Memory size14.6 KiB
https://www.congress.gov/bill/113th-congress/house-bill/2609
 
35
https://www.congress.gov/bill/113th-congress/house-bill/2397
 
34
https://www.congress.gov/bill/113th-congress/house-bill/1947
 
31
https://www.congress.gov/bill/113th-congress/house-bill/4923
 
29
https://www.congress.gov/bill/113th-congress/house-bill/4660
 
27
Other values (874)
1656 

Length

Max length76
Median length75
Mean length59.028146
Min length52

Characters and Unicode

Total characters106959
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique489 ?
Unique (%)27.0%

Sample

1st rowhttps://www.congress.gov/bill/113th-congress/house-resolution/5
2nd rowhttps://www.congress.gov/bill/113th-congress/house-resolution/5
3rd rowhttps://www.congress.gov/bill/113th-congress/house-resolution/5
4th rowhttps://www.congress.gov/bill/113th-congress/house-resolution/5
5th rowhttps://www.congress.gov/bill/113th-congress/house-bill/41

Common Values

ValueCountFrequency (%)
https://www.congress.gov/bill/113th-congress/house-bill/2609 35
 
1.9%
https://www.congress.gov/bill/113th-congress/house-bill/2397 34
 
1.8%
https://www.congress.gov/bill/113th-congress/house-bill/1947 31
 
1.7%
https://www.congress.gov/bill/113th-congress/house-bill/4923 29
 
1.6%
https://www.congress.gov/bill/113th-congress/house-bill/4660 27
 
1.5%
https://www.congress.gov/bill/113th-congress/house-bill/1960 23
 
1.2%
https://www.congress.gov/bill/113th-congress/house-bill/4745 23
 
1.2%
https://www.congress.gov/bill/113th-congress/house-bill/4870 21
 
1.1%
https://www.congress.gov/bill/113th-congress/house-bill/2217 18
 
1.0%
https://www.congress.gov/bill/113th-congress/house-bill/5016 17
 
0.9%
Other values (869) 1554
83.6%
(Missing) 47
 
2.5%

Length

2023-08-28T14:44:00.482430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.congress.gov/bill/113th-congress/house-bill/2609 35
 
1.9%
https://www.congress.gov/bill/113th-congress/house-bill/2397 34
 
1.9%
https://www.congress.gov/bill/113th-congress/house-bill/1947 31
 
1.7%
https://www.congress.gov/bill/113th-congress/house-bill/4923 29
 
1.6%
https://www.congress.gov/bill/113th-congress/house-bill/4660 27
 
1.5%
https://www.congress.gov/bill/113th-congress/house-bill/1960 23
 
1.3%
https://www.congress.gov/bill/113th-congress/house-bill/4745 23
 
1.3%
https://www.congress.gov/bill/113th-congress/house-bill/4870 21
 
1.2%
https://www.congress.gov/bill/113th-congress/house-bill/2217 18
 
1.0%
https://www.congress.gov/bill/113th-congress/house-bill/5016 17
 
0.9%
Other values (869) 1554
85.8%

Most occurring characters

ValueCountFrequency (%)
s 10438
 
9.8%
/ 9928
 
9.3%
o 8473
 
7.9%
t 6289
 
5.9%
l 5807
 
5.4%
g 5436
 
5.1%
e 5110
 
4.8%
n 5089
 
4.8%
h 4826
 
4.5%
1 4608
 
4.3%
Other values (24) 40955
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76561
71.6%
Other Punctuation 15942
 
14.9%
Decimal Number 11719
 
11.0%
Dash Punctuation 2737
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 10438
13.6%
o 8473
11.1%
t 6289
 
8.2%
l 5807
 
7.6%
g 5436
 
7.1%
e 5110
 
6.7%
n 5089
 
6.6%
h 4826
 
6.3%
w 4569
 
6.0%
c 3954
 
5.2%
Other values (10) 16570
21.6%
Decimal Number
ValueCountFrequency (%)
1 4608
39.3%
3 2463
21.0%
2 843
 
7.2%
4 688
 
5.9%
9 568
 
4.8%
7 543
 
4.6%
0 538
 
4.6%
6 530
 
4.5%
5 504
 
4.3%
8 434
 
3.7%
Other Punctuation
ValueCountFrequency (%)
/ 9928
62.3%
. 4202
26.4%
: 1812
 
11.4%
Dash Punctuation
ValueCountFrequency (%)
- 2737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76561
71.6%
Common 30398
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 10438
13.6%
o 8473
11.1%
t 6289
 
8.2%
l 5807
 
7.6%
g 5436
 
7.1%
e 5110
 
6.7%
n 5089
 
6.6%
h 4826
 
6.3%
w 4569
 
6.0%
c 3954
 
5.2%
Other values (10) 16570
21.6%
Common
ValueCountFrequency (%)
/ 9928
32.7%
1 4608
15.2%
. 4202
13.8%
- 2737
 
9.0%
3 2463
 
8.1%
: 1812
 
6.0%
2 843
 
2.8%
4 688
 
2.3%
9 568
 
1.9%
7 543
 
1.8%
Other values (4) 2006
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 10438
 
9.8%
/ 9928
 
9.3%
o 8473
 
7.9%
t 6289
 
5.9%
l 5807
 
5.4%
g 5436
 
5.1%
e 5110
 
4.8%
n 5089
 
4.8%
h 4826
 
4.5%
1 4608
 
4.3%
Other values (24) 40955
38.3%

source_documents
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1859
Missing (%)100.0%
Memory size14.6 KiB

Interactions

2023-08-28T14:43:55.037634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:47.903004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.759410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.655265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.659459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.496716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.418805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.359614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.188376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.134441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:47.993335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.867717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.908809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.756479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.602565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.521605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.454636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.283154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.229309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.092512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.960835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.009551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.845088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.704935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.628548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.546724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.381910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.325193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.178642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.049300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.096838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.927447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.795868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.725546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.638512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.468729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.419330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.270382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.142458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.185815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.008447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.892681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.828327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.723838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.555090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.695681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.374127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.257624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.293847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.117725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.007428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.939567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.822392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.661506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.803977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.481287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.372343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.395048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.223280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.112708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.047532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.924214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.763242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.891423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.569132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.465101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.478263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.307559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.211676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.152523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.007656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.851065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:55.983112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:48.661812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:49.555678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:50.564372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:51.394839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:52.313387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:53.255017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.097327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:43:54.939396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-08-28T14:44:00.579331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
rollnumberclerk_rollnumberyea_countnay_countnominate_mid_1nominate_mid_2nominate_spread_1nominate_spread_2nominate_log_likelihoodchambersessionvote_resultvote_questioncrs_policy_area
rollnumber1.0000.5420.3060.199-0.1240.074-0.085-0.027-0.0810.4450.7520.2530.2700.294
clerk_rollnumber0.5421.0000.2980.225-0.1620.029-0.121-0.001-0.1360.4280.1970.2260.2500.286
yea_count0.3060.2981.0000.013-0.1800.057-0.370-0.160-0.0990.9390.1200.4920.5770.312
nay_count0.1990.2250.0131.000-0.1960.149-0.1170.121-0.5260.7680.1240.4410.5360.264
nominate_mid_1-0.124-0.162-0.180-0.1961.000-0.2550.2410.0200.1080.2840.0670.1670.2910.182
nominate_mid_20.0740.0290.0570.149-0.2551.0000.0220.150-0.1120.2590.0810.1890.2300.130
nominate_spread_1-0.085-0.121-0.370-0.1170.2410.0221.0000.3460.0380.3510.1140.2640.3380.245
nominate_spread_2-0.027-0.001-0.1600.1210.0200.1500.3461.000-0.0650.1220.0130.1890.2200.131
nominate_log_likelihood-0.081-0.136-0.099-0.5260.108-0.1120.038-0.0651.0000.4370.1030.1400.3260.155
chamber0.4450.4280.9390.7680.2840.2590.3510.1220.4371.0000.0810.9920.9860.436
session0.7520.1970.1200.1240.0670.0810.1140.0130.1030.0811.0000.2770.3000.271
vote_result0.2530.2260.4920.4410.1670.1890.2640.1890.1400.9920.2771.0000.8330.132
vote_question0.2700.2500.5770.5360.2910.2300.3380.2200.3260.9860.3000.8331.0000.217
crs_policy_area0.2940.2860.3120.2640.1820.1300.2450.1310.1550.4360.2710.1320.2171.000

Missing values

2023-08-28T14:43:56.156471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-28T14:43:56.490558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-28T14:43:56.696341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

congresschamberrollnumberdatesessionclerk_rollnumberyea_countnay_countnominate_mid_1nominate_mid_2nominate_spread_1nominate_spread_2nominate_log_likelihoodbill_numbervote_resultvote_descvote_questiondtl_descissue_codespeltzman_codesclausen_codescrs_policy_areacrs_subjectscongress_urlsource_documents
0113House12013-01-03122201920.0610.358-0.7470.288-0.303NoneBoehnerNoneElection of the SpeakerNaN[Election of the Speaker of the House][Internal Organization][Miscellaneous Policy]NoneNoneNoneNaN
1113House22013-01-03132241870.0560.197-0.7320.309-0.292HRES5PassedAdopting rules for the One Hundred Thirteenth CongressOn Motion to Table the Motion to ReferNaNNone[Internal Organization][Miscellaneous Policy]Congress[Administrative law and regulatory procedures, Advisory bodies, Appropriations, Budget process, Civil actions and liability, Congressional committees, Congressional operations and organization, Congressional oversight, Constitution and constitutional amendments, Department of Justice, Federal officials, Government ethics and transparency, public corruption, House Committee on Armed Services, House Committee on Foreign Affairs, House Committee on Oversight and Government Reform, House Committee on Standards of Official Conduct, House Committee on Transportation and Infrastructure, House of Representatives, Human rights, Legislative rules and procedure, Marriage and family status, Medicare, Members of Congress, Sex, gender, sexual orientation discrimination, Sports and recreation facilities]https://www.congress.gov/bill/113th-congress/house-resolution/5NaN
2113House32013-01-03142271910.1550.562-0.7240.438-5.349HRES5PassedAdopting rules for the One Hundred Thirteenth CongressOn Ordering the Previous QuestionNaNNone[Internal Organization][Miscellaneous Policy]Congress[Administrative law and regulatory procedures, Advisory bodies, Appropriations, Budget process, Civil actions and liability, Congressional committees, Congressional operations and organization, Congressional oversight, Constitution and constitutional amendments, Department of Justice, Federal officials, Government ethics and transparency, public corruption, House Committee on Armed Services, House Committee on Foreign Affairs, House Committee on Oversight and Government Reform, House Committee on Standards of Official Conduct, House Committee on Transportation and Infrastructure, House of Representatives, Human rights, Legislative rules and procedure, Marriage and family status, Medicare, Members of Congress, Sex, gender, sexual orientation discrimination, Sports and recreation facilities]https://www.congress.gov/bill/113th-congress/house-resolution/5NaN
3113House42013-01-03151942290.1350.9760.771-0.249-4.643HRES5FailedAdopting rules for the One Hundred Thirteenth CongressOn Motion to CommitNaNNone[Internal Organization][Miscellaneous Policy]Congress[Administrative law and regulatory procedures, Advisory bodies, Appropriations, Budget process, Civil actions and liability, Congressional committees, Congressional operations and organization, Congressional oversight, Constitution and constitutional amendments, Department of Justice, Federal officials, Government ethics and transparency, public corruption, House Committee on Armed Services, House Committee on Foreign Affairs, House Committee on Oversight and Government Reform, House Committee on Standards of Official Conduct, House Committee on Transportation and Infrastructure, House of Representatives, Human rights, Legislative rules and procedure, Marriage and family status, Medicare, Members of Congress, Sex, gender, sexual orientation discrimination, Sports and recreation facilities]https://www.congress.gov/bill/113th-congress/house-resolution/5NaN
4113House52013-01-03162281960.1680.695-0.7210.461-5.371HRES5PassedAdopting rules for the One Hundred Thirteenth CongressOn Agreeing to the ResolutionNaNNone[Internal Organization][Miscellaneous Policy]Congress[Administrative law and regulatory procedures, Advisory bodies, Appropriations, Budget process, Civil actions and liability, Congressional committees, Congressional operations and organization, Congressional oversight, Constitution and constitutional amendments, Department of Justice, Federal officials, Government ethics and transparency, public corruption, House Committee on Armed Services, House Committee on Foreign Affairs, House Committee on Oversight and Government Reform, House Committee on Standards of Official Conduct, House Committee on Transportation and Infrastructure, House of Representatives, Human rights, Legislative rules and procedure, Marriage and family status, Medicare, Members of Congress, Sex, gender, sexual orientation discrimination, Sports and recreation facilities]https://www.congress.gov/bill/113th-congress/house-resolution/5NaN
5113House62013-01-0417354670.586-0.2541.333-0.592-93.674HR41PassedTo temporarily increase the borrowing authority of the Federal Emergency Management Agency for carrying out the National Flood Insurance ProgramOn Motion to Suspend the Rules and PassNaN[Public Safety][Budget Special Interest][Government Management]Emergency Management[Budget process, Department of Homeland Security, Disaster relief and insurance, Executive agency funding and structure, Federal Emergency Management Agency (FEMA), Floods and storm protection]https://www.congress.gov/bill/113th-congress/house-bill/41NaN
6113House72013-01-141840300.0000.0000.0000.0000.000HR219PassedTo improve and streamline disaster assistance for Hurricane Sandy, and for other purposesOn Motion to Suspend the Rules and PassNaNNone[Budget Special Interest][Government Management]Emergency Management[Alternative dispute resolution, mediation, arbitration, Child care and development, Disaster relief and insurance, Emergency planning and evacuation, Environmental assessment, monitoring, research, Government employee pay, benefits, personnel management, Historic sites and heritage areas, Homelessness and emergency shelter, Housing and community development funding, Indian social and development programs, Inflation and prices, Natural disasters, Solid waste and recycling, Wages and earnings]https://www.congress.gov/bill/113th-congress/house-bill/219NaN
7113House82013-01-1419300950.776-0.4230.067-0.114-220.033NonePassedNoneOn Approving the JournalNaNNone[Internal Organization][Miscellaneous Policy]NoneNoneNoneNaN
8113House92013-01-1411043970.0000.0000.0000.0000.000NoneFailedNoneOn Motion to AdjournNaNNone[Internal Organization][Miscellaneous Policy]NoneNoneNoneNaN
9113House102013-01-15111293127-0.2770.186-0.2100.130-119.342HRES23PassedProviding for consideration of the bill (H.R. 152) making supplemental appropriations for the fiscal year ending September 30, 2013, and for other purposesOn Ordering the Previous QuestionNaNNone[Budget Special Interest][Government Management]Congress[House of Representatives, Legislative rules and procedure]https://www.congress.gov/bill/113th-congress/house-resolution/23NaN
congresschamberrollnumberdatesessionclerk_rollnumberyea_countnay_countnominate_mid_1nominate_mid_2nominate_spread_1nominate_spread_2nominate_log_likelihoodbill_numbervote_resultvote_descvote_questiondtl_descissue_codespeltzman_codesclausen_codescrs_policy_areacrs_subjectscongress_urlsource_documents
1849113Senate6482014-12-15235754390.209-0.5510.7370.108-1.386PN1753Cloture Motion Agreed toDaniel J. Santos, of Virginia, to be a Member of the Defense Nuclear Facilities Safety Board for a term expiring October 18, 2017On the Cloture MotionNaN[Nuclear Weapons][Government Organization][Foreign and Defense Policy]NoneNonehttps://www.congress.gov/nomination/113th-congress/1753NaN
1850113Senate6492014-12-15235854390.1210.0800.7090.239-0.657PN1099Cloture Motion Agreed toFrank A. Rose, of Massachusetts, to be an Assistant Secretary of State (Verification and Compliance)On the Cloture MotionNaNNone[Government Organization][Foreign and Defense Policy]NoneNonehttps://www.congress.gov/nomination/113th-congress/1099NaN
1851113Senate6502014-12-16235953410.018-0.2160.666-0.689-0.116PN1996Cloture Motion Agreed toSarah R. Saldana, of Texas, to be an Assistant Secretary of Homeland SecurityOn the Cloture MotionNaNNone[Government Organization][Government Management]NoneNonehttps://www.congress.gov/nomination/113th-congress/1996NaN
1852113Senate6512014-12-16236055390.0201.0000.2600.125-12.522PN1996Nomination ConfirmedSarah R. Saldana, of Texas, to be an Assistant Secretary of Homeland SecurityOn the NominationNaNNone[Government Organization][Government Management]NoneNonehttps://www.congress.gov/nomination/113th-congress/1996NaN
1853113Senate6522014-12-16236153400.034-0.1370.655-0.722-0.117PN2123Cloture Motion Agreed toAntony Blinken, of New York, to be Deputy Secretary of StateOn the Cloture MotionNaNNone[Government Organization][Foreign and Defense Policy]NoneNonehttps://www.congress.gov/nomination/113th-congress/2123NaN
1854113Senate6532014-12-1623625538-0.0251.0000.3000.219-9.778PN2123Nomination ConfirmedAntony Blinken, of New York, to be Deputy Secretary of StateOn the NominationNaNNone[Government Organization][Foreign and Defense Policy]NoneNonehttps://www.congress.gov/nomination/113th-congress/2123NaN
1855113Senate6542014-12-16236365280.233-0.9720.220-0.130-22.642PN1986Cloture Motion Agreed toColette Dodson Honorable, of Arkansas, to be a Member of the Federal Energy Regulatory Commission for the remainder of the term expiring June 30, 2017On the Cloture MotionNaN[Energy][Government Organization][Government Management]NoneNonehttps://www.congress.gov/nomination/113th-congress/1986NaN
1856113Senate6552014-12-16236476160.8140.4750.130-0.069-39.591HR5771Bill PassedTo amend the Internal Revenue Code of 1986 to extend certain expiring provisions and make technical corrections, to amend the Internal Revenue Code of 1986 to provide for the tax treatment of ABLE accounts established under State programs for the care of family members with disabilities, and for other purposes.On Passage of the BillNaN[Tax rates][Budget Special Interest][Government Management]Taxation[Alcoholic beverages, Alternative and renewable resources, American Samoa, Budget deficits and national debt, Budget process, Building construction, Business investment and capital, Capital gains tax, Caribbean area, Charitable contributions, Coal, Commuting, Computers and information technology, Congressional oversight, Corporate finance and management, Economic development, Educational facilities and institutions, Electric power generation and transmission, Elementary and secondary education, Employee benefits and pensions, Employee hiring, Energy efficiency and conservation, Financial services and investments, First responders and emergency personnel, Food assistance and relief, Food industry and services, Foreign and international corporations, General taxation matters, Higher education, Housing finance and home ownership, Income tax credits, Income tax deductions, Income tax deferral, Income tax exclusion, Indian social and development programs, Industrial facilities, Insurance industry and regulation, Interest, dividends, interest rates, Land use and conservation, Low- and moderate-income housing, Military personnel and dependents, Mining, Performing arts, Professional sports, Public utilities and utility rates, Puerto Rico, Railroads, Research and development, Residential rehabilitation and home repair, Retail and wholesale trades, Sales and excise taxes, Securities, Small business, Sports and recreation facilities, State and local taxation, Student aid and college costs, Tax administration and collection, taxpayers, Taxation of foreign income, Teaching, teachers, curricula, Television and film, Transportation costs, U.S. and foreign investments, U.S. territories and protectorates, Virgin Islands, Wages and earnings, Worker safety and health]http://hdl.loc.gov/loc.uscongress/legislation.113hr5771NaN
1857113Senate6562014-12-1623655138-0.045-0.9980.889-0.175-3.023PN1345Cloture Motion Agreed toStephen R. Bough, of Missouri, to be United States District Judge for the Western District of MissouriOn the Cloture MotionNaN[Judiciary][Government Organization][Government Management]NoneNonehttps://www.congress.gov/nomination/113th-congress/1345NaN
1858113Senate6572014-12-1623665138-0.109-0.9930.826-0.403-3.157PN1345Nomination ConfirmedStephen R. Bough, of Missouri, to be United States District Judge for the Western District of MissouriOn the NominationNaN[Judiciary][Government Organization][Government Management]NoneNonehttps://www.congress.gov/nomination/113th-congress/1345NaN